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基于随机森林模型的美国白蛾在中国的潜在生境预测(PDF)

《南京林业大学学报(自然科学版)》[ISSN:1000-2006/CN:32-1161/S]

Issue:
2019年06期
Page:
121-128
Column:
研究论文
publishdate:
2019-11-25

Article Info:/Info

Title:
Potential habitat prediction of Hyphantria cunea based on a random forest model in China
Article ID:
1000-2006(2019)06-0121-08
Author(s):
JI Yelin1SU Xiyou1*YU Zhijun2
(1.School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China; 2.General Station of Forest Pest Management, State Forestry and Grassland Administration, Shenyang 110034, China)
Keywords:
Hyphantria cunea random forest model potential habitat climate scenarios spatial distribution
Classification number :
S763.3
DOI:
10.3969/j.issn.1000-2006.201808046
Document Code:
-
Abstract:
【Objective】 Hyphantria cunea is extremely harmful to plant that are highly susceptible to outbreaks of infection. Predicting the potential habitat of H. cunea is essential for its prevention and control, and this prediction is based on a random forest model, which predicts and analyzes spatial distribution, the importance of environmental factors, occurrence area and migration situation of H. cunea under the current climate and based on the data from the 1950s; overall, these data can provide a theoretical basis for effective prevention and control for this pest. 【Method】 County and municipal data on the occurrence of H. cunea from 2011 to 2017 were obtained, and non-occurrence points were made with the create random points tool of ArcGIS. By adopting the principle of the random forest model, 19 climate and 5 environmental factors(altitude, slope, aspect, vegetation coverage and effective photosynthetic radiation)were selected, and the environmental variables of occurrence and non-occurrence points were extracted by extracting values using the points tool of ArcGIS. Then, the altitude, slope and aspect were discretized. This study used R to simulate a potential habitat distribution model for H. cunea from 2011 to 2030, and the ROC curve was used to check the accuracy of the model. The order of importance of environmental factors was determined using this model. The future habitat distribution of H. cunea in China was also predicted under two climate scenarios(RCP2.6 and RCP4.5)for 2041-2060(2050s). 【Result】 ROC curve analysis indicated that the use of the random forest model to predict the potential habitat distribution of H. cunea achieves high precision; the AUC of training and testing data was 0.997 and 0.963, respectively. In the current period, the potential distribution(suitable areas)of H. cunea accounted for 8.74% of the total study area; the areas of low, medium, high and extremely high suitable accounted for 41.47%, 20.85%, 18.90% and 18.78%, respectively. The suitable areas were mainly concentrated in the southeast of northern China, north of central and southern China, north of eastern China, and south of northeastern China. In order of importance, the environmental factors that influence the potential habitat distribution of H. cunea are as follows: altitude, vegetation coverage, average temperature in the wettest season, and maximum temperature in the warmest month. Under RCP2.6 for the 2050s, the potential distribution of H. cunea will account for 14.38% of the total study area, whereas the low, medium, high and extremely high suitable areas will account for 50.87%, 20.37%, 16.49% and 12.27%, respectively; under RCP4.5, the potential distribution of H. cunea will account for 19.06% of the total study area, and the low, medium, high and extremely high suitable areas will account for 51.14%, 15.11%, 20.36% and 13.39%, respectively. In the 2050s, the centroids of the potential habitats of H. cunea will migrate 93.65 km toward the north on an average. New suitable areas will include Heilongjiang, Jilin, Sichuan, Hubei, Shanxi, Henan, eastern Inner Mongolia and Taiwan. 【Conclusion】H. cunea is adapted to live at low altitudes, in areas with high temperatures, in rainy summers, and in areas with rich forest resources. With climate change, the potential habitat of this pest will spread toward the north of China and inland areas with a high humidity. The area and degree of its occurrence will gradually increase.

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Last Update: 2019-11-30